Electric Mobility: Smart Transportation in Smart Cities

2014 - 2015One of the mega trends over the past century has been humanity’s move towards cities. Public Administration and Municipalities are facing a challenging task, to harmonize a sustainable urban development offering to people in city the best living conditions. Smart cities are now considered a winning urban strategy able to increase the quality of life by using technology in urban space, both improving the environmental quality and delivering better services to the citizens. Mobility is a key element to support this new approach in the growth of the cities. In fact, transport produces several negative impacts and problems for the quality of life in cities, such as, pollution, traffic and congestion. Therefore, Sustainable Mobility is one of the most promising topics in smart city, as it could produce high benefits for the quality of life of almost all the city stakeholders. The boldest and imminent challenge awaiting mobility in smart cities is the introduction of the electricity as energy vector instead of fossil fuels, concerning both the collective and the private transports. Electric public transport include electric city buses, trolleybuses, trams (or light rail), passenger trains and rapid transit (metro/subways/undergrounds, etc.). Even though railway systems are the most energy efficient than other transport modes, the enhancement of energy efficiency is an important issue to reduce their contributions to climate change further as well as to save and enlarge competition advantages involved. One key means for improving energy efficiency is to deploy advanced systems and innovative technologies. Additionally, electrification of the private road transport has emerged as a trend to support energy efficiency and CO2 emissions reduction targets. According to the International Energy Agency, in order to limit average global temperature increases to 2°C - the critical threshold that scientists say will prevent dangerous climate change -, by 2050, 21% of carbon reductions must come from the transport sector. Full electric vehicles (EVs) use electric motor and battery energy for propulsion, which has higher efficiency and lower operating cost compared to the conventional internal combustion engine vehicle. Today, there are more than 20 models offered by different brands covering different range of sizes, styles, prices and powertrains to suit the wider range of consumers as possible. The continuous development of lithium ion battery and of fast charging technology will be the major facilitators for EVs roll out in the very near future. However, the present EVs industry meets many technical limitations, such as high initial price, long battery recharge time, limited charging facilities and driving range. Although it is desirable a fast development from the start of electric mobility, its impact on the existing power grid must be assessed beforehand to see if it is necessary prior an adjustment of power infrastructure or/and the introduction of new services in the power grid. In fact, the interconnection of EVs on the power grid for charging their batteries potentially introduces negative impacts on grid operation: uncontrolled charging can significantly increase average load in the existing power systems, with problems in terms of reliability and overloads. If uncontrolled EV charging is added to the system, this can have effects both at the distribution and at the generation level. Controlled or smart charging will allow a much greater number of cars in the cities, avoiding local overload and allowing a faster EVs penetration without requiring an imminent improvement of the electricity generating and grid capacity. Smart charging might also allow load balancing both at sub-station and at the grid level, particularly with charging at peak wind supply times. This kind of use of EV battery capacity for storing electric energy may ease the integration of large scale intermittent electricity sources such as renewable energy sources. The proposed PhD Dissertation is developed in the context just described, mainly focusing the attention on the impact that electric mobility will have on the power systems and the effectiveness of solutions aimed to increase the reliability and resilience in the smart grid. In particular, it is addressed a scenario analysis regarding the electric vehicles charging management and some innovative solutions to increase energy efficiency in electrified transport systems. The first chapter emphasizes on the key aspects related to the sustainable mobility in the smart cities of the future. It provides a brief overview on the transport sector energy consumption expected in the next years. In particular, the chapter shows the significant contribution that the electrification of urban transport may provide to the sustainable mobility, and the serious concerns related to its impact on existing power systems. Chapter 2 proposes a solution method for an optimal generation rescheduling and load-shedding (GRLS) problem in microgrids in order to determine a stable equilibrium state following unexpected outages of generation or sudden increase in demand. The chapter mainly focuses on the mathematical formulation of the GRLS problem and the proposed solution algorithm. Finally, simulations results carried out by using a real case study data are presented and discussed. In Chapter 3, a simple and effective methodology is proposed to analyze data acquired during the fulfillment of the COSMO research project, and to identify typical load pattern for the EVs charging. The chapter also presents a novel scheduling problem formulation, flattening the demand load profile and minimizing the EVs charging costs, according to the electricity prices during the day. Finally, some simulations results are discussed, showing the effectiveness of the proposed methodology. Chapter 4 introduces some innovative solutions for energy efficiency in urban railway systems focusing, in particular, on energy storage systems and eco-drive operations in metro networks. The mathematical formulation of these optimization problems and the proposed solution algorithms are illustrated and discussed. The obtained results are part of the activity carried out in the SFERE research project. Finally, Chapter 5 ends the Dissertation with some concluding remarks and further developments of the proposed research activity. [edited by author]Una delle grandi tendenze nel corso del secolo scorso è stata la concentrazione della popolazione nelle città. Attualmente, le Pubbliche Amministrazioni e i Comuni si trovano ad affrontare un compito impegnativo per armonizzare uno sviluppo urbano sostenibile e offrire agli abitanti delle città le migliori condizioni di vita. Le smart cities sono ormai considerate una strategia urbana vincente in grado di aumentare la qualità della vita utilizzando la tecnologia, sia per il miglioramento della qualità ambientale che per fornire servizi migliori ai cittadini. A tale scopo, la mobilità risulta essere un elemento chiave per sostenere questo nuovo approccio nella crescita delle città. Infatti, i sistemi di trasporto urbano producono diversi effetti negativi sulla qualità della vita urbana, come ad esempio, inquinamento, traffico e congestione. Pertanto, la mobilità sostenibile è uno degli argomenti più interessanti per le smart cities, in quanto in grado produrre elevati benefici per la qualità della vita di quasi tutte le parti interessate degli agglomerati urbani. La sfida più audace e imminente per la mobilità nelle smart cities del futuro è l'introduzione dell'elettricità come vettore energetico al posto dei combustibili fossili, per quanto riguarda sia il trasporto collettivo che quello privato. I mezzi per il trasporto pubblico comprendono autobus elettrici, filobus, tram, treni passeggeri e trasporto rapido (metropolitane, etc.). Anche se i sistemi di trasporto su ferro sono più efficienti rispetto ad altri modi di trasporto, l’incremento dell'efficienza energetica è un tema importante per ridurre ulteriormente il loro contributo alle emissioni inquinanti e al consumo di energia. Le più promettenti soluzioni per migliorarne l'efficienza energetica consistono nell’implementazione di sistemi avanzati per il recupero dell’energia di frenata e tecnologie di controllo innovative. D’altro canto, l'elettrificazione del trasporto individuale su strada è emersa come una tendenza finalizzata a sostenere gli obiettivi di efficienza energetica e di riduzione delle emissioni di CO2. Secondo l'Agenzia Internazionale per l'Energia, al fine di limitare, entro il 2050, l'aumento della temperatura media globale a 2 °C - la soglia critica che gli scienziati suggeriscono di non superare per evitare pericolosi cambiamenti climatici -, il 21% delle riduzioni di biossido di carbonio deve provenire dal settore trasporti. I veicoli elettrici (EV) utilizzano un motore elettrico e l'energia accumulata nelle batterie per la propulsione, in modo da avere una maggiore efficienza e minori costi operativi rispetto ai veicoli convenzionali con motore a combustione interna. Oggi, esistono in commercio più di 20 modelli offerti da diverse case produttrici che coprono una ampia gamma di modelli che differiscono per dimensione, stile, prezzo e motorizzazione in modo da soddisfare il maggior numero di consumatori possibile. Il continuo sviluppo delle batterie al litio e delle tecnologie di ricarica rapida saranno i principali fattori abilitanti per la diffusione degli EV in un futuro molto prossimo. Tuttavia, l'attuale industria dei veicoli elettrici incontra molte limitazioni tecnico-economiche, come elevati costi, autonomia e tempi di ricarica della batteria, capillarità delle infrastrutture di ricarica. Sebbene sia auspicabile un rapido sviluppo della mobilità elettrica, il suo impatto sulla rete elettrica esistente deve essere investigato a fondo per verificare la necessità di potenziamenti delle infrastrutture e/o l'introduzione di nuovi servizi nella rete elettrica. Infatti, l'interconnessione dei veicoli elettrici con la rete di distribuzione dell’energia necessaria per la ricarica delle batterie può causare effetti negativi sul normale funzionamento del sistema elettrico: una ricarica degli EV non controllata può aumentare significativamente il carico medio negli impianti esistenti, introducendo problemi di affidabilità e sovraccarico. La ricarica intelligente o controllata degli EV consente, invece, di gestire un numero molto maggiore di autovetture elettriche nelle città, riducendo le possibilità di sovraccarico locale e di velocizzare la penetrazione della mobilità elettrica senza che rendere necessari imminenti potenziamenti dei sistemi di produzione di energia elettrica e incrementi della capacità di rete. La ricarica intelligente, inoltre, può anche influire sul bilanciamento del carico sia a livello della sottostazione elettrica che a livello di rete di distribuzione, in particolare quando si verificano molte sessioni di ricarica nelle ore di punta. Infatti, l’utilizzo della capacità della batteria degli EV per l’accumulo di energia elettrica può facilitare l'integrazione su larga scala delle fonti di energia non programmabili, come quelle rinnovabili. Il lavoro di tesi si sviluppa nel contesto di riferimento appena descritto, focalizzando l'attenzione soprattutto sull'impatto che la mobilità elettrica ha sui sistemi elettrici e sull'efficacia di nuove soluzioni finalizzate all’incremento dell'affidabilità nelle smart grids. In particolare, viene proposta un'analisi di scenario per quanto riguarda la gestione intelligente delle ricariche dei veicoli elettrici e alcune soluzioni innovative per aumentare l'efficienza energetica nei sistemi di trasporto elettrificati. Il primo capitolo sottolinea gli aspetti chiave relativi alla mobilità sostenibile nelle smart cities del futuro e fornisce una breve panoramica sul consumo energetico del settore trasporti previsto nel prossimo futuro. In particolare, vengono evidenziate da un lato il significativo contributo che l'elettrificazione dei trasporti urbani può fornire alla causa della mobilità sostenibile, e dall’altro, le gravi preoccupazioni legate all’impatto sui sistemi elettrici esistenti di un notevole incremento della domanda. Il Capitolo 2 propone un metodo per la soluzione del problema congiunto di scheduling dei generatori e load shedding (GRLS) all’interno di microgrids portando in conto l’incertezza sia sulla domanda che lato generazione. Il fine è determinare un nuovo stato di equilibrio stabile in seguito a guasti, riduzione della generazione da fonte rinnovabile o improvviso aumento della domanda. Il capitolo si concentra principalmente sulla formulazione matematica del problema GRLS e sull'algoritmo di soluzione proposto. Infine, sono presentati e commentati i risultati di simulazione basati su un caso studio reale. Nel Capitolo 3, è proposta una metodologia semplice ed efficace per identificare profili di carico tipico relativi alla ricarica di veicoli elettrici: in particolare, l’analisi condotta si basa sull’analisi dei dati acquisiti durante lo svolgimento del progetto di ricerca COSMO. Il capitolo, inoltre, introduce una formulazione matematica del problema dello scheduling delle ricariche dei veicoli elettrici, che garantisce un appiattimento del profilo di carico e riduce allo stesso tempo il costo della ricarica per gli utenti. Infine, sono commentati i risultati delle simulazioni eseguite dimostrando l'efficacia della metodologia proposta. Il Capitolo 4 introduce alcune soluzioni innovative per l'efficienza energetica nei sistemi di trasporto urbani: l’attenzione viene posta, in particolare, sui sistemi di accumulo dell’energia e sulla condotta di guida Eco-Drive in reti metropolitane. In dettaglio, nel capitolo, vengono introdotti e commentati la formulazione matematica dei problemi di ottimizzazione proposti e i rispettivi algoritmi di soluzione. I risultati ottenuti fanno parte delle attività svolte nell’ambito del progetto di ricerca SFERE. Infine, il Capitolo 5 conclude la tesi con alcune osservazioni finali e con i possibili sviluppi dell'attività di ricerca proposta. [a cura dell'autore]XIV n.s

Optimization of Islanded Microgrid Operation

Presently a lot of effort is being deployed in the area of microgrid development. In this aspect, the work presented here is in the direction of developing and coordinating various operational modules in an isolated microgrid system. The work presented in this report looks at the prospects of incorporating a consumer side load-scheduling algorithm that works in conjunction with the unit commitment and economic load dispatch. The unit commitment and economic load dispatch are run a day in advance to determine generator outputs for the following day. From the microgrid operator point of view, the load side scheduling helps reduce the stress on the system especially during peak hours thereby ensuring system stability and security. From the consumers’ point of view, the dynamic electricity prices within a day, which are a reflection of this time varying stress on the system, encourage them to endorse such a scheme and reduce their bills incurred. Owing to unpredictable weather conditions, running unit commitment and economic load dispatch in advance does not guarantee planned real-time generation in the microgrid scenario. Such variability in forecasted generation must be handled in any microgrid, while accounting for load demand uncertainties. To address this issue a load side energy management system and power balance scheme is proposed in this paper. The objective is to ascertain uninterrupted power to critical loads while managing other non-critical loads based on their priorities

Use of Semidefinite Optimization in Emergency Power System Control

This thesis is a study of long-term voltage stability in power systems following a severe disturbance. An optimization method for solving the optimal power flow problem is used on power system models. The method tries to optimize the generated active power while fulfilling the requirements from the consumers. Dynamic limitations of the generators are taken into account and an extension of the optimization routine which optimizes over several time steps is constructed. The results from the optimization are then applied to a power system simulator during a scenario where the system is bound to collapse. Whether the stability of the system is improved or not is analysed and discussed

Assessment and Enhancement of Power System Security using Soft Computing and Data Mining Approaches

The power system is a complex network with numerous equipment’s interconnected for it’s reliable operation. These power system networks are forced to operate under highly stressed conditions closer to their limits. One of the key objective of the power system operators is to provide safe, economic and reliable power to it’s consumers. However, such network experiences perturbations due to many factors. These perturbations may lead to system collapse or even black out, which impacts the reliability of the system. Thus, one of the major aspect for the secure operation of the system can be achieved through security assessment. In this context, the power system static security assessment is necessary to evaluate the security status under contingency scenario. One of the approach for the security assessment is by contingency ranking, where the severity of a specific contingency is computed and ranked with highest severity to the lowest one. Initially, this approach is implemented using several load flow methods in order to identify the limit violations. However, these approaches are complex, time consuming and not feasible for real time implementation. These approaches are applied to a specific system operating condition. Thus in this context, this thesis focusses to implement soft computing and data mining approaches for security assessment by contingency ranking and classification approach. Along with the security assessment, this thesis also focusses on a control mechanism approach for the security enhancement under contingency scenario using evolutionary computing techniques. In this thesis, the various aspects of the power system security such as it’s assessment, and it’s enhancement are studied. The conventional contingency ranking approach by NRLF method is presented for the security assessment. In order to predict the system severity, a ranking module is designed with two neural network models namely, MFNN and RBFN for security assessment under different load conditions. Both neural network models are quite accurate in predicting the performance indices in less time. Another aspect of power system static security assessment is by classification approach, where the security states are classified into secure, critically secure, insecure and highly insecure. This approach helps in proper security monitoring. Thus, this thesis also presents the design and implementation of two security pattern classifier models namely the decision tree and the random forest classifiers. The classifiers are trained and tested with several security patterns generated in an offline mode. The proposed models are compared with MLP, RBFN and SVM classifier models in order to prove their efficiency in classifying the security levels. Further, this thesis work also focusses on a control mechanism for security enhancement under N-1 line outage contingency scenario. Initially contingency analysis is carried out under N-1 line outage case and critical contingencies are identified. The objective is to reschedule the generators with minimum fuel cost in such a way that the overloaded lines are relieved from stress. In order to enhance the power system security, an evolutionary computing algorithm, namely an enhanced cuckoo search algorithm is proposed for the contingency constrained economic load dispatch. To study the robustness and effectiveness of the proposed algorithm, the results are compared with CS, BA and PSO algorithms

A review of World Bank lending for electric power

This paper presents the result of an evaluation of about 300 power projects financed by the World Bank and IDA between 1965 and 1983. The study shows a declining trend in power sector performance in spite of Bank involvement in the sector. It recommends greater emphasis on : improving productive and allocative efficiency; increasing incentives for enhanced utility efficiency; strengthening of power-energy-macroeconomic linkages; improving investment planning to achieve a better balance between generation and distribution, and giving greater emphasis to rehabilitation and maintenance. Sector restructuring and institutional reform is also recommended to improve the social compact between government, consumers and the electric utility.Banks&Banking Reform,Environmental Economics&Policies,Economic Theory&Research,Health Monitoring&Evaluation,Business Environment

Flexible Scheduling in Middleware for Distributed rate-based real-time applications - Doctoral Dissertation, May 2002

Distributed rate-based real-time systems, such as process control and avionics mission computing systems, have traditionally been scheduled statically. Static scheduling provides assurance of schedulability prior to run-time overhead. However, static scheduling is brittle in the face of unanticipated overload, and treats invocation-to-invocation variations in resource requirements inflexibly. As a consequence, processing resources are often under-utilized in the average case, and the resulting systems are hard to adapt to meet new real-time processing requirements. Dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling often has a high run-time cost because certain decisions are enforced on-line. Furthermore, under conditions of overload tasks can be scheduled dynamically that may never be dispatched, or that upon dispatch would miss their deadlines. We review the implications of these factors on rate-based distributed systems, and posits the necessity to combine static and dynamic approaches to exploit the strengths and compensate for the weakness of either approach in isolation. We present a general hybrid approach to real-time scheduling and dispatching in middleware, that can employ both static and dynamic components. This approach provides (1) feasibility assurance for the most critical tasks, (2) the ability to extend this assurance incrementally to operations in successively lower criticality equivalence classes, (3) the ability to trade off bounds on feasible utilization and dispatching over-head in cases where, for example, execution jitter is a factor or rates are not harmonically related, and (4) overall flexibility to make more optimal use of scarce computing resources and to enforce a wider range of application-specified execution requirements. This approach also meets additional constraints of an increasingly important class of rate-based systems, those with requirements for robust management of real-time performance in the face of rapidly and widely changing operating conditions. To support these requirements, we present a middleware framework that implements the hybrid scheduling and dispatching approach described above, and also provides support for (1) adaptive re-scheduling of operations at run-time and (2) reflective alternation among several scheduling strategies to improve real-time performance in the face of changing operating conditions. Adaptive re-scheduling must be performed whenever operating conditions exceed the ability of the scheduling and dispatching infrastructure to meet the critical real-time requirements of the system under the currently specified rates and execution times of operations. Adaptive re-scheduling relies on the ability to change the rates of execution of at least some operations, and may occur under the control of a higher-level middleware resource manager. Different rates of execution may be specified under different operating conditions, and the number of such possible combinations may be arbitrarily large. Furthermore, adaptive rescheduling may in turn require notification of rate-sensitive application components. It is therefore desirable to handle variations in operating conditions entirely within the scheduling and dispatching infrastructure when possible. A rate-based distributed real-time application, or a higher-level resource manager, could thus fall back on adaptive re-scheduling only when it cannot achieve acceptable real-time performance through self-adaptation. Reflective alternation among scheduling heuristics offers a way to tune real-time performance internally, and we offer foundational support for this approach. In particular, run-time observable information such as that provided by our metrics-feedback framework makes it possible to detect that a given current scheduling heuristic is underperforming the level of service another could provide. Furthermore we present empirical results for our framework in a realistic avionics mission computing environment. This forms the basis for guided adaption. This dissertation makes five contributions in support of flexible and adaptive scheduling and dispatching in middleware. First, we provide a middle scheduling framework that supports arbitrary and fine-grained composition of static/dynamic scheduling, to assure critical timeliness constraints while improving noncritical performance under a range of conditions. Second, we provide a flexible dispatching infrastructure framework composed of fine-grained primitives, and describe how appropriate configurations can be generated automatically based on the output of the scheduling framework. Third, we describe algorithms to reduce the overhead and duration of adaptive rescheduling, based on sorting for rate selection and priority assignment. Fourth, we provide timely and efficient performance information through an optimized metrics-feedback framework, to support higher-level reflection and adaptation decisions. Fifth, we present the results of empirical studies to quantify and evaluate the performance of alternative canonical scheduling heuristics, across a range of load and load jitter conditions. These studies were conducted within an avionics mission computing applications framework running on realistic middleware and embedded hardware. The results obtained from these studies (1) demonstrate the potential benefits of reflective alternation among distinct scheduling heuristics at run-time, and (2) suggest performance factors of interest for future work on adaptive control policies and mechanisms using this framework

Space Station Freedom automation and robotics: An assessment of the potential for increased productivity

This report presents the results of a study performed in support of the Space Station Freedom Advanced Development Program, under the sponsorship of the Space Station Engineering (Code MT), Office of Space Flight. The study consisted of the collection, compilation, and analysis of lessons learned, crew time requirements, and other factors influencing the application of advanced automation and robotics, with emphasis on potential improvements in productivity. The lessons learned data collected were based primarily on Skylab, Spacelab, and other Space Shuttle experiences, consisting principally of interviews with current and former crew members and other NASA personnel with relevant experience. The objectives of this report are to present a summary of this data and its analysis, and to present conclusions regarding promising areas for the application of advanced automation and robotics technology to the Space Station Freedom and the potential benefits in terms of increased productivity. In this study, primary emphasis was placed on advanced automation technology because of its fairly extensive utilization within private industry including the aerospace sector. In contrast, other than the Remote Manipulator System (RMS), there has been relatively limited experience with advanced robotics technology applicable to the Space Station. This report should be used as a guide and is not intended to be used as a substitute for official Astronaut Office crew positions on specific issues